Proteins are the workhorses of the cell that perform biological functions by interacting with other proteins. Many statistical methods for protein-protein interaction (PPI) have been studied without considering time-dependent changes in networks and the functionalities. These time-dependent functional and topological changes in the network are very crucial for identifying malfunctioning regulatory pathways at different disease stages. I introduced a novel method that models PPI networks as being dynamic in nature and evolving time-varying multivariate distribution with Conditional Random Fields (CRF). This research is directed towards implementing this new combinatorial algorithm on massively parallel architectures such as Graphics Processing Units (GPUs) for efficient computations for large scale bioinformatics datasets. I compared Conditional Random Fields (CRF) and the proposed novel method using CRF combined with the Block Coordinate Descent algorithm for human protein-protein interaction data set. Both are implemented on GPU-Accelerated Computing Architecture and the proposed novel method showed the advantages in predicting protein-protein interaction sites. I also show that the proposed approach is more efficient in 6.13% than standalone CRF++ in predicting protein-protein interaction sites.